B.J. Boom

The Effect of Image Resolution on the Performance of a Face Recognition System

In this paper we investigate the effect of image resolution on the error rates of a face verification system. We do not restrict ourselves to the face recognition algorithm only, but we also consider the face registration. In our face recognition system, the face registration is done by finding landmarks in a face image and subsequent alignment based on these landmarks. To investigate the effect of image resolution we performed experiments where we varied the resolution. We investigate the effect of the resolution on the face recognition part, the registration part and the entire system. This research also confirms that accurate registration is of vital importance to the performance of the face recognition algorithm. The results of our face recognition system are optimal on face images with a resolution of 32 times 32 pixels

Secure pairing with biometrics

Secure pairing enables two devices that share no prior context with each other to agree upon a security association, which they can use to protect their subsequent communication. Secure pairing offers guarantees of the association partner identity and it should be resistant to eavesdropping and to a man-in the middle attack. We propose the SAfE pairing system, a user friendly solution to this problem. Details are presented along with a discussion of the security features, experimental validation with two types of biometric data (face recognition and hand grip pressure pattern) and a usability analysis for face recognition biometric pairing.

Virtual illumination grid for correction of uncontrolled illumination in facial images

Face recognition under uncontrolled illumination conditions is still considered an unsolved problem. In order to correct for these illumination conditions, we propose a virtual illumination grid (VIG) approach to model the unknown illumination conditions. Furthermore, we use coupled subspace models of both the facial surface and albedo to estimate the face shape. In order to obtain a representation of the face under frontal illumination, we relight the estimated face shape. We show that the frontal illuminated facial images achieve better performance in face recognition. We have performed the challenging Experiment 4 of the FRGCv2 database, which compares uncontrolled probe images to controlled gallery images. Our illumination correction method results in considerably better recognition rates for a number of well-known face recognition methods. By fusing our global illumination correction method with a local illumination correction method, further improvements are achieved.

Model-Based Illumination Correction for Face Images in Uncontrolled Scenarios

Face Recognition under uncontrolled illumination conditions is partly an unsolved problem. Several illumination correction methods have been proposed, but these are usually tested on illumination conditions created in a laboratory. Our focus is more on uncontrolled conditions. We use the Phong model which allows us to model ambient light in shadow areas. By estimating the face surface and illumination conditions, we are able to reconstruct a face image containing frontal illumination. The reconstructed face images give a large improvement in performance of face recognition in uncontrolled conditions.

Model-based reconstruction for illumination variation in face images

We propose a novel method to correct for arbitrary illumination variation in the face images. The main purpose is to improve recognition results of face images taken under uncontrolled illumination conditions. We correct the illumination variation in the face images using a face shape model, which allows us to estimate the face shape in the face image. Using this face shape, we can reconstruct a face image under frontal illumination. These reconstructed images improve the results in face identification. We experimented both with face images acquired under different controlled illumination conditions in a laboratory and under uncontrolled illumination conditions.

Combining Illumination Normalization Methods for Better Face Recognition

Face Recognition under uncontrolled illumination conditions is partly an unsolved problem. There are two categories of illumination normalization methods. The first category performs a local preprocessing, where they correct a pixel value based on a local neighborhood in the images. The second category performs a global preprocessing step, where the illumination conditions and the face shape of the entire image are estimated. We use two illumination normalization methods from both categories, namely Local Binary Patterns and Model-based Face Illumination Correction. The preprocessed face images of both methods are individually classified with a face recognition algorithm which gives us two similarity scores for a face image. We combine the similarity scores using score-level fusion, decision-level fusion and hybrid fusion. In our previous work, we show that combining the similarity score of different methods using fusion can improve the performance of biometric systems. We achieved a significant performance improvement in comparison with the individual methods.